Does a standardized preoperative algorithm of clinical data improve outcomes in patients with ovarian cancer? A quality improvement project

标准化的术前临床数据算法能否改善卵巢癌患者的预后?一项质量改进项目

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Abstract

OBJECTIVE: To evaluate the potential impact of a standardized preoperative algorithm on outcomes of patients with suspected ovarian cancer. METHODS: From January 1 to December 31, 2013, patients with suspected ovarian cancer were triaged to primary debulking surgery or neoadjuvant chemotherapy/interval debulking surgery (NACT/IDS) based on a comprehensive review of preoperative clinical data as part of a quality improvement project. Demographics, surgical, and postoperative data were collected. RESULTS: A total of 110 patients with newly diagnosed ovarian cancer were identified: 68 (62%) underwent PDS with an 85% optimal debulking rate. The 30-day readmission rate was 14.7% with a 2.9% 60-day mortality rate. Forty-two patients (38%) underwent NACT. Two patients (4.8%) died before receiving NACT. Thirty-five patients have undergone IDS with an 89% optimal debulking rate. The 30-day readmission rate was 8.5% with a 5.7% 60-day mortality rate after IDS. CONCLUSIONS: Although it is difficult to predict which patients will undergo optimal debulking at the time of PDS, surgical morbidity and mortality can be decreased by using NACT in select patients. The initiation of a quality improvement project has contributed to an improvement in patient outcomes at our institution.

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